where \(\delta \)  returns a score depending on the overlap location between \(R_i\)  and a prediction \(P_j\)  (flat bias, front bias, middle bias, and back bias). Further details could be found in the original manuscript [1]}. The precision is similarly defined. It seeks to assess the quality of the predictions by rewarding a detector in the presence of a TP and penalizing it when facing an FP. It is computed as follows,
\(\text{Precision}_{T}=\frac{1}{N_p}\sum _{i=1}^{N_p}{\text{Precision}_T(R, P_i)},\) 
